Nope, the Py2 RF was saved with joblib!
The SO response might work for standard pickling though, I'll give that a try,
thanks!
On Fri, Jan 23, 2015 at 11:18 AM, Sebastian Raschka <se.rasc...@gmail.com>
wrote:
> Sorry, I think my previous message was a little bit ambiguous.
> What I would try is:
> 1) Unpickle the original pickle file in Python 2
> 2) Pickle it via joblib
> 3) Load it in Python 3
> (I think you only did step 3), right? Sorry for the confusion).
> I also just saw a related SO post that might be very helpful:
> http://stackoverflow.com/questions/11305790/pickle-incompatability-of-numpy-arrays-between-python-2-and-3
>
> <http://stackoverflow.com/questions/11305790/pickle-incompatability-of-numpy-arrays-between-python-2-and-3>
> Best,
> Sebastian
>> On Jan 22, 2015, at 5:10 PM, jni.s...@gmail.com wrote:
>>
>> Hi Sebastian,
>>
>> Thanks for the response, but actually joblib doesn't work either:
>>
>> In [1]: from sklearn.externals import joblib
>>
>> In [2]: rf = joblib.load('rf-1.joblib')
>> ---------------------------------------------------------------------------
>> error Traceback (most recent call last)
>> <ipython-input-3-2c47f0ec1d5b> in <module>()
>> ----> 1 rf = joblib.load('rf-1.joblib')
>>
>> /Users/nuneziglesiasj/anaconda/envs/py3k-gala/lib/python3.3/site-packages/sklearn/externals/joblib/numpy_pickle.py
>> in load(filename, mmap_mode)
>> 417 'ignoring mmap_mode "%(mmap_mode)s"
>> flag passed'
>> 418 % locals(), Warning, stacklevel=2)
>> --> 419 unpickler = ZipNumpyUnpickler(filename,
>> file_handle=file_handle)
>> 420 else:
>> 421 unpickler = NumpyUnpickler(filename,
>> file_handle=file_handle,
>>
>> /Users/nuneziglesiasj/anaconda/envs/py3k-gala/lib/python3.3/site-packages/sklearn/externals/joblib/numpy_pickle.py
>> in __init__(self, filename, file_handle)
>> 306 NumpyUnpickler.__init__(self, filename,
>> 307 file_handle,
>> --> 308 mmap_mode=None)
>> 309
>> 310 def _open_pickle(self, file_handle):
>>
>> /Users/nuneziglesiasj/anaconda/envs/py3k-gala/lib/python3.3/site-packages/sklearn/externals/joblib/numpy_pickle.py
>> in __init__(self, filename, file_handle, mmap_mode)
>> 264 self._dirname = os.path.dirname(filename)
>> 265 self.mmap_mode = mmap_mode
>> --> 266 self.file_handle = self._open_pickle(file_handle)
>> 267 Unpickler.__init__(self, self.file_handle)
>> 268 try:
>>
>> /Users/nuneziglesiasj/anaconda/envs/py3k-gala/lib/python3.3/site-packages/sklearn/externals/joblib/numpy_pickle.py
>> in _open_pickle(self, file_handle)
>> 309
>> 310 def _open_pickle(self, file_handle):
>> --> 311 return BytesIO(read_zfile(file_handle))
>> 312
>> 313
>>
>> /Users/nuneziglesiasj/anaconda/envs/py3k-gala/lib/python3.3/site-packages/sklearn/externals/joblib/numpy_pickle.py
>> in read_zfile(file_handle)
>> 66 # We use the known length of the data to tell Zlib the size of
>> the
>> 67 # buffer to allocate.
>> ---> 68 data = zlib.decompress(file_handle.read(), 15, length)
>> 69 assert len(data) == length, (
>> 70 "Incorrect data length while decompressing %s."
>>
>> error: Error -3 while decompressing data: incorrect header check
>>
>>
>> The very same commands work fine in Py2:
>>
>> In [1]: from sklearn.externals import joblib
>>
>> In [2]: rf1 = joblib.load('rf-1.joblib')
>>
>> In [3]:
>>
>>
>> Is this unexpected?
>>
>>
>>
>>
>> On Fri, Jan 23, 2015 at 1:57 AM, Sebastian Raschka <se.rasc...@gmail.com
>> <mailto:se.rasc...@gmail.com>> wrote:
>>
>> Hi, Juan,
>>
>> It's been some time, but I remember that I had similar issues. I think it
>> has to do with the numpy arrays that specifically cause problems in pickle.
>> (http://bugs.python.org/issue6784)
>>
>> You could try to use joblib (which should also be more efficient):
>>
>> >>> from sklearn.externals import joblib
>> >>> joblib.dump(clf, 'filename.pkl')
>> >>> clf = joblib.load('filename.pkl')
>>
>> (http://scikit-learn.org/stable/modules/model_persistence.html)
>>
>>
>> Best,
>> Sebastian
>>
>> > On Jan 22, 2015, at 8:50 AM, jni.s...@gmail.com wrote:
>> >
>> > Hi all,
>> >
>> > I'm working on a project that depends on sklearn. I've been up test
>> > coverage (which includes saving a RandomForest, so far using joblib
>> > serialization), and now I wanted to make the project Python 3-compatible.
>> > However, the final roadblock is the sharing of RF objects: I can't load
>> > the Python 2-serialized RFs with Python 3 tests. Of course, the test
>> > outcome depends on the exact RF that was created a while back. Is there
>> > any way around this?
>> >
>> > Thanks!
>> >
>> > Juan.
>> >
>> >
>> > ------------------------------------------------------------------------------
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